On June 22nd we launched the AgentX evaluation framework on Product Hunt and ended the day at #1. Thank you to everyone who upvoted, commented, and tried it.
Evaluate AI agents before they fail
The pitch is simple: catch the problems before they reach production.
- Test suites and evaluations. Create test suites for your agent, run evaluations against them, and pinpoint exactly where things go wrong.
- Full observability and traceability. Every run is traced end to end, so you can see what the agent reasoned, which tools it called, and what came back.
- AI analysis that suggests fixes. The analysis does more than flag a failure. It explains the likely cause and proposes a fix, like an AI doctor for your agents.
- Simulate across LLM providers. Run the same agent on multiple models and compare performance, cost, and latency side by side, so the choice of LLM is a decision backed by data.
- Run eval before deploy. Wire evaluation into your release process. Think CI/CD for AI agents.
Watch the launch video
The video above walks through the whole flow in a few minutes: building a test suite, running an evaluation, reading the AI analysis, and comparing models.
If you build agents and want to try it, start at agentx.so or open an issue on the open-source repo.